Finding Average Regret Ratio Minimizing Set in Database

A Fully Dynamic Algorithm for k-Regret Minimizing Sets

Selecting a small set of representatives from a large database is important in many applications such as multi-criteria decision making, web search, and recommendation. The $k$-regret minimizing set ($k$-RMS) problem was recently proposed for representative tuple discovery. Specifically, for a large database $P$ of tuples with multiple numerical attributes, the $k$-RMS problem returns a size-$r$ subset $Q$ of $P$ such that, for any possible ranking function, the score of the top-ranked tuple in $Q$ is not much worse than the score of the $k$\textsuperscript{th}-ranked tuple in $P$. Although th…

向量数据库中的向量数据交易

Trading Vector Data in Vector Databases

针对向量数据库中的向量数据交易问题，提出分层赌博机框架，联合优化检索配置与定价，实现对数遗憾与次线性遗憾，并在四个真实数据集上验证其优于现有方法。

近似双次模 regret 最小化在 Billboard 和社交媒体广告中

Approximately Bisubmodular Regret Minimization in Billboard and Social Media Advertising

本文提出了一种近似双次模影响模型，用于解决多模式广告中非单调、非次模的 regret 最小化问题，并通过实验验证了其在减少总 regret 和满足广告需求方面的有效性。

K-Regret Queries Using Multiplicative Utility Functions

The k-regret query aims to return a size-k subset S of a database D such that, for any query user that selects a data object from this size-k subset S rather than from database D, her regret ratio is minimized. The regret ratio here is modeled by the relative difference in the optimality between the locally optimal object in S and the globally optimal object in D. The optimality of a data object in turn is modeled by a utility function of the query user. Unlike traditional top-k queries, the k-regret query does not minimize the regret ratio for a specific utility function. Instead, it consider…

No DBA? No regret! Multi-armed bandits for index tuning of analytical and HTAP workloads with provable guarantees

Automating physical database design has remained a long-term interest in database research due to substantial performance gains afforded by optimised structures. Despite significant progress, a majority of today's commercial solutions are highly manual, requiring offline invocation by database administrators (DBAs) who are expected to identify and supply representative training workloads. Even the latest advancements like query stores provide only limited support for dynamic environments. This status quo is untenable: identifying representative static workloads is no longer realistic; and phys…

Rank-Regret Minimization

Multi-criteria decision-making often requires finding a small representative set from the database. A recently proposed method is the regret minimization set (RMS) query. RMS returns a size $r$ subset $S$ of dataset $D$ that minimizes the regret-ratio (the difference between the score of top-1 in $S$ and the score of top-1 in $D$, for any possible utility function). RMS is not shift invariant, causing inconsistency in results. Further, existing work showed that the regret-ratio is often a made-up number and users may mistake its absolute value. Instead, users do understand the notion of rank. …

高维 regret 最小化

High-dimensional Regret Minimization

FHDR 是一种高效处理高维数据集的交互查询框架，通过减少交互轮次和执行时间，实现了可扩展的 regret 最小化。

Sorting-based Interactive Regret Minimization

As an important tool for multi-criteria decision making in database systems, the regret minimization query is shown to have the merits of top-k and skyline queries: it controls the output size while does not need users to provide any preferences. Existing researches verify that the regret ratio can be much decreased when interaction is available. In this paper, we study how to enhance current interactive regret minimization query by sorting mechanism. Instead of selecting the most favorite point from the displayed points for each interaction round, users sort the displayed data points and send…